Bayesian NS EoS study using full nuclear posterior distributions and consistent crust modeling finds increased surface thickness and crustal moment of inertia relative to prior work.
The equation of state for dense nucleonic matter from a metamodeling. I. Foundational aspects
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abstract
A metamodeling for the nucleonic equation of state (EOS), inspired from a Taylor expansion around the saturation density of symmetric nuclear matter, is proposed and parameterized in terms of the empirical parameters. The present knowledge of nuclear empirical parameters is first reviewed in order to estimate their average values and associated uncertainties, and thus defining the parameter space of the metamodeling. They are divided into isoscalar and isovector type, and ordered according to their power in the density expansion. The goodness of the metamodeling is analyzed against the predictions of the original models. In addition, since no correlation among the empirical parameters is assumed a priori, all arbitrary density dependences can be explored, which might not be accessible in existing functionals. Spurious correlations due to the assumed functional form are also removed. This meta-EOS allows direct relations between the uncertainties on the empirical parameters and the density dependence of the nuclear equation of state and its derivatives, and the mapping between the two can be done with standard Bayesian techniques. A sensitivity analysis shows that the more influential empirical parameters are the isovector parameters $L_{sym}$ and $K_{sym}$, and that laboratory constraints at super-saturation densities are essential to reduce the present uncertainties. The present metamodeling for the EOS for nuclear matter is proposed for further applications in neutron stars and supernova matter.
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A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
Emulator-assisted Bayesian inference of an extended Skyrme EDF, jointly constrained by nuclear observables, ab initio calculations, and NICER data, produces posteriors yielding consistent neutron star crust and core properties with a provided multivariate Gaussian for bulk nuclear matter parameters.
Hybrid neutron-star equations of state remain sensitive to the low-density nucleonic model at transition densities around 2ρ₀, with model spread in radius and tidal deformability exceeding observational uncertainty by factors of ~1.8 and ~1.4.
The paper evaluates how triangular versus two-L-shaped geometries, arm lengths, and presence of low-frequency instruments affect the science reach of the Einstein Telescope for compact binaries, multi-messenger events, and stochastic backgrounds.
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Properties of the neutron star crust informed by nuclear structure data
Bayesian NS EoS study using full nuclear posterior distributions and consistent crust modeling finds increased surface thickness and crustal moment of inertia relative to prior work.
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A Physics Informed Bayesian Neural Network for the Neutron Star Equation of State
A physics-informed Bayesian neural network learns neutron-star equations of state from theoretical priors and constraints, then generates posterior mass-radius and mass-tidal-deformability distributions consistent with NICER radii and 2-solar-mass limits.
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Emulator-Assisted Nuclear DFT Inference and Its Consequences for the Structure of Neutron Stars
Emulator-assisted Bayesian inference of an extended Skyrme EDF, jointly constrained by nuclear observables, ab initio calculations, and NICER data, produces posteriors yielding consistent neutron star crust and core properties with a provided multivariate Gaussian for bulk nuclear matter parameters.
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Sensitivity of Neutron Star Observables to Transition Density in Hybrid Equation-of-State Models
Hybrid neutron-star equations of state remain sensitive to the low-density nucleonic model at transition densities around 2ρ₀, with model spread in radius and tidal deformability exceeding observational uncertainty by factors of ~1.8 and ~1.4.
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Science with the Einstein Telescope: a comparison of different designs
The paper evaluates how triangular versus two-L-shaped geometries, arm lengths, and presence of low-frequency instruments affect the science reach of the Einstein Telescope for compact binaries, multi-messenger events, and stochastic backgrounds.